Medical evaluation machine learning workflows and processes

ABSTRACT

Systems and methods for processing electronic imaging data obtained from medical imaging procedures are disclosed herein. Some embodiments relate to data processing mechanisms for medical imaging and diagnostic workflows involving the use of machine learning techniques such as deep learning, artificial neural networks, and related algorithms that perform machine recognition of specific features and conditions in imaging data. In an example, a deep learning model is selected for automated image recognition of a particular medical condition on image data, and applied to the image data to recognize characteristics of the particular medical condition. Based on the characteristics recognized by the automated image recognition on the image data, an electronic workflow for performing a diagnostic evaluation of the medical imaging study may be modified, updated, or prioritized.

PRIORITY

This application is a continuation of U.S. patent application Ser. No.15/809,786, filed on Nov. 10, 2017, which is a continuation of U.S.patent application Ser. No. 15/168,567, filed on May 31, 2016, nowissued as U.S. Pat. No. 9,846,938, which claims the benefit of U.S.Provisional Application Ser. No. 62/169,339, filed Jun. 1, 2015, all ofwhich applications are incorporated by reference herein its entirety.

TECHNICAL FIELD

Embodiments pertain to techniques and systems for processing electronicdata obtained from imaging or other diagnostic and evaluative medicalprocedures. Some embodiments relate to data processing mechanisms formedical imaging and diagnostic workflows involving the use of machinelearning techniques such as deep learning, artificial neural networks,and related algorithms that perform machine recognition of specificfeatures and conditions in imaging and other medical data.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system configuration enabled for processing,coordinating, and directing medical imaging data in connection with amachine learning workflow according to an example described herein.

FIG. 2 illustrates system operations in a workflow for analysis ofmedical imaging data produced from a medical imaging procedure accordingto an example described herein.

FIG. 3 illustrates a flowchart of operations performed in a traumaprotocol workflow for processing medical imaging data according to anexample described herein.

FIG. 4 illustrates a diagram of anatomical areas used for unbundlingmedical images with a trauma protocol workflow according to an exampledescribed herein.

FIG. 5 illustrates a flowchart of a process for detecting a medicalcondition from image data in a machine learning image analysis workflowaccording to an example described herein.

FIG. 6 illustrates a flowchart of a process for training an algorithmused in detecting a medical condition from image data in a machinelearning image analysis workflow according to an example describedherein.

FIG. 7 illustrates a flowchart of example workflow operations performedfor processing data of a medical study using results from a machinelearning analysis according to an example described herein.

FIG. 8 illustrates a flowchart of additional workflow operationsperformed for processing data of a medical study using results from amachine learning analysis according to an example described herein.

FIG. 9 illustrates a system configuration of a medical imaging dataprocessing system arranged to process medical imaging data with machinelearning operations according to an example described herein.

FIG. 10 illustrates an example of a machine configured to performcomputing operations according to an example described herein.

DETAILED DESCRIPTION

The following description and the drawings sufficiently illustratespecific embodiments to enable those skilled in the art to practicethem. Other embodiments may incorporate structural, logical, electrical,process, and other changes. Portions and features of some embodimentsmay be included in, or substituted for, those of other embodiments.

The present disclosure illustrates various techniques and configurationsthat enable the integration and use of machine learning analysis in adata-driven image evaluation workflow. For example, machine learninganalysis (such as trained models of image detection of certain medicalconditions) may be performed upon medical imaging procedure dataproduced as part of a medical imaging study. The medical imagingprocedure data may include image data captured by an imaging modality,and order data (such as data indicating a request for a radiologicalimage read), each produced to facilitate a medical imaging evaluation(such as a radiology read to be performed by a radiologist or adiagnostic evaluation by another qualified medical professional).

For example, the machine learning analysis may receive and processimages from medical imaging procedure data, to identify trainedstructures, conditions, and conditions within images of a particularstudy. The machine learning analysis may result in the automateddetection, indication, or confirmation of certain medical conditionswithin the images, such as the detection of urgent or life-criticalmedical conditions, clinically serious abnormalities, and other keyfindings. Based on the result of the machine learning analysis, themedical evaluation workflow for the images and the associated imagingprocedure may be prioritized, or otherwise changed or modified. Further,the detection of the medical conditions may be used to assist theassignment of the medical imaging data to particular evaluators, theevaluation process for the medical imaging data, or implement otheractions prior to, or concurrent with, the medical imaging evaluation (orthe generation of a data item such as a report from such medical imagingevaluation).

The evaluation workflow activities which may be modified in connectionwith the techniques described herein, may include activities for a studyhaving images that indicate a critical, urgent, or time-sensitivemedical condition, such as: prioritizing the study in an electronicworklist of an evaluating user; re-assigning the study to a specialistwho is proficient in diagnosing the critical medical condition; alertinga transmitting medical facility, an operations center, or one ormultiple medical evaluators regarding the criticality of the study;identifying or labeling specific image locations in the study outputbased on the conditions identified in the study; and rearranging orreorganizing display characteristics of images or reporting data for thestudy based on the conditions identified in the study. These steps maybe implemented by electronic (e.g., computer-implemented) operations inspecialized software that control the operation of an overall system,through data operations at client and server computers, networkcommunications, and related processing and messaging functions.

As further discussed herein, the machine learning analysis may beprovided on behalf of any number of machine learning algorithms andtrained models, including but not limited to deep learning models (alsoknown as deep machine learning, or hierarchical models) that have beentrained to perform image recognition tasks, particularly for certaintypes of medical conditions upon medical images of human anatomy andanatomical representations. As used herein, the term “machine learning”is used to refer to the various classes of artificial intelligencealgorithms and algorithm-driven approaches that are capable ofperforming machine-driven (e.g., computer-aided) identification oftrained structures, with the term “deep learning” referring to amultiple-level operation of such machine learning algorithms usingmultiple levels of representation and abstraction. However, it will beapparent that the role of the machine learning algorithms that areapplied, used, and configured in the presently described medical imagingworkflow and analysis activities may be supplemented or substituted byany number of other algorithm-based approaches, including variations ofartificial neural networks, learning-capable algorithms, trainableobject classifications, and other artificial intelligence processingtechniques.

In some of the following examples, reference is made to radiologymedical imaging procedures (e.g., computed tomography (CT), magneticresonance imaging (MRI), Ultrasound, and X-ray procedures, etc.) anddiagnostic evaluation of the images produced from such imagingprocedures that would be performed with an image evaluation (e.g.,radiology read) by a licensed and credentialed radiologist. It will beunderstood that the applicability of the presently described techniquesand systems will extend to a wide variety of imaging data (and otherdata representations) produced by various medical procedures andspecialties, including those not involving traditional radiology imagingmodalities. Such specialties include, but are not limited, to pathology,medical photography, medical data measurements such aselectroencephalography (EEG) and electrocardiography (EKG) procedures,cardiology data, neuroscience data, preclinical imaging, and other datacollection procedures occurring in connection with telemedicine,telepathology, remote diagnostics, and other applications of medicalprocedures and medical science. Accordingly, the performance of the datarecognition and workflow modification techniques described herein mayapply to a variety of medical image data types, settings, and use cases,including captured static images and multi-image (e.g. video)representations.

FIG. 1 provides an illustration of an example medical imaging systemconfiguration 100 (e.g., a radiology imaging configuration), whichenables the processing of data from medical imaging procedures accordingto an example described herein. The medical imaging system configuration100 may be used for capturing medical image data in one location and forreviewing medical images associated with the data in another location.The medical imaging system configuration 100 may include manygeographically separated imaging devices and many image reviewterminals. The medical imaging system configuration 100, in a radiologysetting, may be embodied as a remote teleradiology system connected to aplurality of healthcare locations, as a localized radiology system usedin a single hospital, healthcare provider network, or private radiologypractice. The medical imaging system configuration 100 may also operateas an information processing network used to process data fromrespective imaging procedures regardless of the location of an eventualimaging evaluation.

For purposes of illustration, the medical imaging system configuration100 depicted in FIG. 1 includes an imaging system 104, an imaging orderprocessing system 102, an image review system 106, and a machinelearning analysis system 108. The imaging system 104, for example, mayinclude an imaging device 120, such as a CT scanner, a MRI scanner, oranother imaging system (e.g., a radiology imaging modality). Using anenergy source such as x-rays or magnetic fields, for example, theimaging device 120 may capture image data associated with a subject 122(e.g., a patient).

The imaging device 120 may be controlled by a technician 126 at themedical facility through the use of a workstation terminal or otherelectronic input control 124. Prior to the technician 126 conducting theimaging procedure for a patient, information may be entered into theelectronic input control 124. Information from an electronic medicalrecord (EMR) or healthcare information system (HIS) may also be accessedor updated for the imaging procedure. Relevant information and metadatafor the imaging procedure may be placed within the image data itself, orwithin another data store for further access and processing. Forexample, the imaging device 120 may produce radiological imagesgenerally consistent with the Digital Imaging and Communications inMedicine (DICOM) format, other industry-accepted standards, orproprietary standards.

Consistent with the appropriate image format, the images produced by theimage data source may include metadata. This metadata may be generatedby the imaging device 120, from input collected by the electronic inputcontrol 124, or from input from a HIS or EMR. Further, a series ofimages produced by the image data source may be obtained directly by theimaging device 120 in the facility shown in FIG. 1, or may betransferred in whole or in part from another image capturing deviceconnected to the imaging device 120 or the medical facility's localnetwork. The imaging data source may also include data transmittedthrough use of a local facility imaging server (not shown), such as aDICOM server or other Picture Archiving and Communication System (PACS).The metadata within each imaging data file may include identificationinformation such as a patient identifier and an identifier of the seriesof images, in addition to information about the type of imaging modalityand the techniques used to obtain the images. Further, for imagesformatted according to the DICOM standard, data fields such as a uniqueimage identifier, a unique study identifier, the patient's name, and thefacility from which the image originates may be included.

The image data generated by the imaging device 120 may include a seriesof two-dimensional images, with the collection of some identifiableseries of images typically referred to as a “study”. In someimplementations, the image data may be used to produce athree-dimensional model that can be further manipulated and reformattedfor generating two-dimensional (or three-dimensional) images. In otherimplementations, the image data may include three-dimensional models orgraphical data generated by the imaging device 120 or intermediateprocessing systems. Image data captured by the imaging device 120 may bestored and processed by the imaging order processing system 102 oranother local or remote imaging device server (e.g., one or morecomputers with a processor and a memory), and may be provided to othersystems and computers in the medical imaging system configuration 100through network 130 (e.g., an intranet or the Internet).

In some implementations, medical imaging procedure data provided to theimaging order processing system 102 results in data being stored andprocessed by one or more computers. For example, the imaging orderprocessing system 102 may determine that the medical imaging proceduredata is to be forwarded to a particular computer associated with anevaluating user 142 (e.g., a radiologist workstation) at an image reviewsystem 106. As shown, image data may be provided by the imaging orderprocessing system 102 through the network 130 to the image review system106. Additionally, the medical imaging procedure data provided to theimaging order processing system 102 results in the image data or relatedmedical data being processed by the machine learning analysis system108. This data may be processed by the machine learning analysis system108 prior to, in parallel with, or at the same time as the provision orassignment of the image data to the image review system 106.

The image review system 106, for example, may include an image displayserver 144 (e.g., one or more computers with a processor and a memory),a display device 146 (e.g., a monitor), and input devices 148A-148B(e.g., keyboards, computer mice, joysticks, touch interfaces, voicerecognition interfaces, and the like). In some implementations, imagedata may be processed by the image display server 144 and visuallypresented to the evaluating user 142 as one or more images at thedisplay device 146. Using the input devices 148A-148B, the evaluatinguser 142 may interact with the presented images, for example, bymanipulating one or more user controls included in a graphical userinterface presented at the display device 146 in association with theimages. For example, the evaluating user 142 may view an image (or aseries of related images), and may specify one or more imageadjustments, such as zooming, panning, rotating, changing contrast,changing color, changing view angle, changing view depth, changingrendering or reconstruction technique, and the like. By viewing andinteracting with presented image data and with the user interface, forexample, the evaluating user 142 may indicate, select, confirm, or inputa diagnostic finding value related to a radiological imaging procedureperformed on the subject 122.

The machine learning analysis system 108, for example, may also includea data processing server 152 (e.g., one or more computers with aprocessor and a memory). In some implementations, medical imagingprocedure data (or images or individual data representations from suchdata) may be processed by a compiled binary or other software executedwith the processor and the memory of the data processing server 152, toperform specialized image recognition operations, among otheroperations. The binary or other software executed with the dataprocessing server 152 may implement one or more machine learning models(such as a deep learning algorithm) on the medical imaging proceduredata, based on the use of model input data 154 and a trained detectionalgorithm 156. In some examples, the trained detection algorithm 156 mayprocess historical data inputs from the model input data 154 and currentdata inputs from the medical imaging procedure data at different levelsof the model (such as at deeper/multiple levels of the learningalgorithm).

In some implementations, data indications are produced by the dataprocessing server 152 of the machine learning analysis system 108 toeffect processing and changes for the subsequent workflows andevaluation activities of the medical imaging procedure data. In someimplementations, the data processing server 152 may establishdescriptors, markings, annotations, or additional metadata for images ofthe medical imaging procedure data; in other examples, the dataprocessing server 152 may indicate the presence of particular identifiedconditions, the absence of certain identified conditions, thelikelihood/probability/or other certainty score of such identifiedconditions, and other related outputs from the operation of arecognition algorithm on the medical imaging procedure data.

When the imaging order processing system 102 receives the image, it mayprocess the image with an image server, for further handling in theevaluation workflow. This processing may include compressing orconverting the image to a different format using a compressor/convertercomponent. This image server may also operate to extract metadata fromeach image file in a series of images. For example, the extractedmetadata may include header data for the image providing patientinformation and medical facility (e.g., hospital) information for thefacility that sent the image. The image server may then store all orpart of the extracted information in a study record that may becorrelated with appropriate orders and studies. The imaging orderprocessing system 102 may operate to process related orders or correlatea particular order (and order data) with a particular set of studyimages (and image data). In some examples, the imaging order processingsystem 102 operates to perform a lateral and horizontal movement ofstudies between an onsite facility and a remote/cloud location with aclosely orchestrated feed utilizing HL7 (Health Level 7) and DICOMstandards.

As discussed herein, the operations occurring at the imaging orderprocessing system 102 and the image review system 106, including theassignment of a particular study to a particular image review system (orthe prioritization of a particular study to a particular image reviewsystem) may be affected by the outputs from the operation of therecognition algorithm on the medical imaging procedure data. Forexample, the evaluation priority of a study within a worklist at animage review system 106 may be changed to a high status based on atime-sensitive identified medical condition such as an intracranialhemorrhage, pulmonary embolism, and the like. Likewise, the imagingorder processing system 102 may perform additional or substitute studyassignments, data transfers, or other processing with the image reviewsystem 106 based on a time-sensitive identified medical condition.

In other examples, identified studies with some level of urgency orimportance may include additional workflow processing actions thatenable assignment to respective image review systems operated by aplurality of evaluators. For example, the identification or detection ofa trauma condition may be used to unbundle multiple portions of a studyand expedite placement (and evaluation priority) of groups of unbundledimages to evaluator worklists. FIGS. 3 and 4 and the associated traumaprotocol described below provides examples of a technique to unbundleportions of a study into separate evaluative studies based on regions ofanatomy (such as for images captured above the clavicle or anotheridentified body area) to allow multiple evaluators to begin evaluation,and to integrate a combination of the evaluation results back to therequesting medical facility.

FIG. 2 illustrates a system operations diagram 200 of an exampleworkflow for generating and routing a set of data produced from aparticular medical imaging study (e.g., a radiology study) with use of atrained image recognition model 240 applied by a machine learning system230 according to an example described herein. The system operationsdiagram 200 is depicted as including image data 206 and order data 210originating from data of a medical imaging procedure (produced from animaging modality 202 or a data store 203 at one or more medical imagingfacilities 201), with the combination of image data and order datacollectively referred to as imaging procedure data 204. It will beunderstood, however, that the imaging procedure data 204 may also beaccompanied, integrated, or associated with information from medicalinformation systems (e.g., EMR data, HIS data, and the like) that is notnecessarily produced from the medical imaging procedure.

The system operations diagram 200 illustrates a series of operationsexecutable with an image processing system, such as the medical imagingsystem configuration 100 or specific components of the imaging orderprocessing system 102 and the machine learning analysis system 108.These operations include the receipt and processing of the imagingprocedure data 204 (e.g., radiology study data, including one or both ofa radiology order and a radiology imaging data) originating from aparticular medical imaging facility or imaging source of the medicalimaging facilities 201. This imaging procedure data 204 is processed toobtain identifying data associated with the medical imaging procedure,including an identification of imaging characteristics, type of theimaging procedure, and associated information related to the evaluationof the imaging data. For example, the medical imaging procedure data mayinclude image data 206 and image metadata 208, where the image metadata208 may include identification information such as a patient identifierand an identifier of the series of images, in addition to informationabout the type of imaging modality and the techniques used to obtain theimages. The imaging procedure data 204 also may include order data 210for an associated order to perform the diagnostic evaluation of theimage data. For example, the order data 210 may be associated with datafrom an HL7 Order Message (ORM) sent when a healthcare provider requestsa service, procedure, or treatment for a patient.

The imaging procedure data 204 may be provided to or assigned within themachine learning system 230 for further use and processing of the imagedata 206 and the order data 210. For example, the machine learningsystem 230 may implement automated image recognition through a trainedimage recognition model 240 with use of a processing algorithm 234 andother condition detection logic 232. Additionally, the conditiondetection logic 232 may select certain processing algorithms or imagerecognition models based on the characteristics of the medical imagingprocedure indicated by order data 210 or image metadata 208. As oneexample, an image recognition model relevant to analysis of a medicalcondition within a human heart may be only applied to certain types ofimages captured from a patient's abdomen as indicated by the imagemetadata 208, whereas such an image recognition models would not beapplied to images captured from outside a patient's abdomen. As anotherexample, the condition detection logic 232 may perform a review ofcertain conditions based on the type of preliminary medical inquiries,known conditions, or findings indicated within the information of theorder data 210.

In some examples, distinct processing algorithms and trained imagerecognition processing may be used to detect the characteristics ofrespective medical conditions; in other examples, a common model orseries of algorithms may be used to detect or measure the likelihood oneor multiple of many identifiable medical conditions. In some scenarios,additional medical data 270 (e.g., data separate from the particularmedical imaging procedure), such as information from a patient's medicalhistory or records, previous imaging evaluations (such as prior imagesin medical image data 272 and reports in medical report data 274), maybe evaluated by the machine learning system 230 to further improve theaccuracy and operation of the processing algorithm 234.

The machine learning system 230 may be used to generate an indication ofone or more medical conditions detected from image data, and generateassociated details and launch processing activities for such medicalconditions. The details for such medical conditions may include aconfidence level for the presence of certain conditions (e.g., a scorethat corresponds to a level of recognition of whether certain conditionsare detected), identification of specific features or areas in the imagein which certain conditions are detected or likely to occur,identification of images in the study in which certain conditions aredetected or likely to occur, and similar identifications andindications.

The trained image recognition model 240, for example, may be embodied bya computer-generated deep learning model trained by numerous historicalstudies and expert results. These studies and results may include thesame format and content as the medical imaging study to be evaluated, ormay take a derivative form. The training of the trained imagerecognition model 240 thus may be provided as a result of earlierevaluation actions at the evaluation system 260, or other human createdor verified results from prior workflow activities. With use of thetrained image recognition model 240, the machine learning system 230 isable to produce accurate and detailed results of machine detectedobjects through a data-driven cycle of training, processing, and expertverification of results.

Results data from the evaluation of images of the particular study withthe machine learning system 230 may be provided to a data assignmentsystem 250, with such results data used for purposes of affecting anassignment or evaluation of the study at one or more selectedevaluators. For example, the study and its associated data may beassigned to one or more selected evaluators for diagnosticinterpretation, analysis, or other human evaluation of the image data206, with the results data from the machine learning system 230 used tomodify evaluation activities or outputs. Additionally, based on thedetection or designation of a severe medical condition (such as a traumadesignation), portions of the study may be unbundled and divided up fordesignation, processing, and transmission to computing systemsassociated with a plurality of selected evaluators.

The data assignment system 250 also may maintain a series of evaluatorworklists 252 that are used to propagate the assignment of studies torespective evaluators, with the evaluator worklists 252 and accompanyinggraphical user interface outputs being affected (e.g., reordered andadjusted) by the results data from the machine learning system 230. Thedata assignment system 250 also may use a set of assignment logic andrules 254 to determine the appropriate assignment of studies to therespective evaluators, which may be affected by the results data fromthe machine learning system 230. For example, a serious andtime-sensitive medical condition identified by the machine learningsystem 230 in a study may result in the study (and its associated dataoperations and processes) being escalated, prioritized, unbundled, oralerted within multiple of the evaluator worklists 252, to reduce theamount of time it takes for at least one evaluator to begin evaluationof the study in a timely manner.

Further, the data assignment system 250 can operate to provide imagingdata to an evaluation system 260 operated by a respective evaluator. Theevaluation system 260 may include a worklist 262 of assigned studies toreview by a particular evaluating user; an image viewer 264 to outputand control the display of various images from the image data; andreporting functions 266 to collect and compile a diagnostic report formedical findings from the image data. The results data provided from themachine learning system 230 may be used to modify the order,orientation, hanging, or positioning of particular images, series, orcomparison studies (for example, to most quickly indicate or displayimages including significant medical findings). These modifications maybe implemented in a display or graphical user interface automatically,or in response to user selection (e.g., in response to alerts anduser-selectable options in a graphical user interface that can quicklyimplement the modifications).

Although not expressly depicted within FIG. 2, other data flows may beestablished or updated between the machine learning system 230 and othercomponents of the data assignment system 250 and evaluation system 260.These data flows may propagate information relating to imageannotations, preliminary image findings and report data, order datamodifications, and the like, and data values for these fields ofinformation may be implemented in connection with the image viewer 264or other aspects of the assignment logic and rules 254 or the reportingfunctions 266. Additionally, the operations performed in connectionwithin FIG. 2 may be implemented with features of a Picture ArchivingCommunication System (PACS) or Radiology Information System (RIS),features of a HIS or data informatics system, or other components of amedical image procedure management system. In other examples, the datafrom the machine learning system may be communicated or propagatedthroughout the system in the system operations diagram 200 to facilitiesand other users as part of display preferences, report data, or otherrelevant data indications.

The prioritization of a study in the evaluator worklists 252 is merelyone example of operations that may occur in a workflow which are drivenby the detection of a medical condition. Other examples of modificationsto the workflow may include the implementation of a trauma protocol thatoperates to unbundle and distribute image series or sets of images frommultiple anatomical locations to multiple evaluators. Other examples ofmodifications to the workflow may include prioritization and otherprocessing activities to certain specialists within a stroke or cardiaccondition workflow or like workflows tailored for time-sensitive (orspecialized) medical conditions.

FIG. 3 illustrates flowchart 300 of operations performed in a traumaprotocol workflow for processing medical imaging data according to anexample described herein. As shown, the example flowchart 300 may beused for implementing a trauma protocol that may be used in response toidentification of a particular urgency (e.g., either from traumaidentified by a referring medical facility or as part of imagerecognition in a deep learning workflow). For example, a study may beidentified as trauma by a referring medical facility according tooperational procedures or medical criterion. Trauma cases may beautomatically “unbundled’ and assigned to multiple radiologists to readconcurrently for faster results, as described in the following examples.

In an example, the assigning an urgency of trauma protocol may beprovided based on the identification of a trauma condition in a studyfrom a machine-learning based evaluation of one or more images of thestudy (operation 310). In response to the identification of the traumacondition, the order (e.g., radiology read order) and any associatedimages (e.g., radiology images) may be designated in a computer systemas under a trauma protocol (operation 320).

With the application of the trauma protocol, an assignment workflow mayoperate to automatically “unbundle” images in a study (or in associatedstudies) that include multiple body parts (operation 330). Based on theresults of the unbundling, and the identified sets of unbundled images,the portions of the study (and the accompanying sets of images) can beassigned and transmitted to multiple radiologists to be read andevaluated concurrently, simultaneously, or in an expedited manner(operation 340).

Additionally, in response to designating the study as a trauma protocol,the study may be escalated to the top of one or more evaluator worklists(operation 350). The evaluators may be instructed, prompted, and guidedto treat trauma protocol cases with a same level of urgency andturnaround time commitments as are applied for other time-sensitiveprotocols, such as a stroke protocol. For example, a turnaround timetarget may be assigned and monitored for respective evaluators(operation 360), with re-assignment or alerts being implemented if thisturnaround time target (e.g., 20 minutes) is not met.

In further examples, the trauma protocol may be used to generate a callor other electronic communication to a referring or requesting medicalfacility of critical findings. For example, call priority may be placedon providing results for Head/Cervical Spine studies in connection withthe trauma protocol designation. In further examples, this call orelectronic communication is configurable to enable receipt of phone callor other electronic communication which includes information on allnegative and positive findings for the study.

FIG. 4 illustrates a diagram of anatomical areas 400 used for unbundlingmedical images with a trauma protocol workflow according to an exampledescribed herein. As discussed above, the application of the traumaprotocol may automatically “unbundle” cases that include multiple bodyparts and send them to multiple radiologists to be read concurrently formultiple body regions. The images that are identified from disparatebody regions may correspond to the listing of images in body regions(e.g., depicted in the diagram of anatomical areas 400), based on thefollowing classifications: Neuro: head, face, orbits, cervical spine,and neck, areas above the clavicle; Body: chest abdomen and pelvis,thoracic and lumbar spine; Lower extremities: feet, legs; Upperextremities: arms, hands.

FIG. 5 illustrates a flowchart 500 of a process for detecting a medicalcondition from image data in a machine learning image analysis workflow,which may be performed in an image data processing system such as thesystems depicted in FIGS. 1 and 2, according to an example describedherein. This process may be initiated through the receipt of a requestfor a medical imaging evaluation from a medical facility, for example,at a radiology practice or at a teleradiology provider. In otherexamples, portions of this preparation and assignment process may beinitiated as a standalone process, such as may be performed by a thirdparty vendor to validate or evaluate imaging procedure data. Further,aspects of the process may be performed at the medical facilityperforming the medical imaging procedure (such as with trained imagedetection processing logic integrated into an intermediate image datastore or image modality).

In the flowchart 500, various operations are performed to conduct thedetection of conditions within a workflow using a trained imagerecognition model, such as a trained deep learning algorithm. Theworkflow operations are commenced with the training of the image dataalgorithm of the machine learning model (operation 510), such as withuse of image data sets, report data, and other data inputs for traininga deep learning model. The results of the training may provide a binaryor algorithm state that can be re-used (and re-deployed) for analysis ofa plurality of subsequent studies, images, and workflow operations.

The operations of the workflow continue with the processing of theimaging data associated with a medical imaging study (operation 520).This processing may involve image extraction, conversion, ortransformation operations, in order to obtain input image data forevaluation with the trained image data recognition algorithm. Theprocessing of the imaging data, for example, may involve the operationsneeded to begin image recognition upon one or multiple images from oneor multiple series of a medical imaging study.

The operations of the flowchart 500 continue with the processing of textand other non-image content associated with the medical imaging study(operation 530). The trained image data recognition algorithm mayevaluate the text and non-image content at lower levels of the deeplearning algorithm, for example, to help influence (and direct) thedetection of particular image features and medical conditions inrespective images of a study. Using the image and non-image dataassociated with the medical imaging study, the trained image datarecognition algorithm operates to identify anatomical features andapplicable learning models that are suitable for the particular image(operation 540). For example, different image recognition models may bemore suited to different anatomical features or imaged areas, and theapplication of the most applicable learning models can be used to detectthe most likely or common medical conditions for those anatomicalfeatures or imaged areas (and, correspondingly, to exclude attempts todetect medical conditions that are not applicable to certain types ofimages, imaging scans, and the like).

After performance of the identification and the tailoring of thealgorithm to the input data, the trained image data recognition model isutilized to generate a score (e.g., confidence score), ranking, or othermetric of detected medical conditions within the imaging data (operation550). Based on this score, ranking, or other metric of detected medicalconditions, a set of positive or negative findings for certain medicalconditions may be determined (operation 560). For example, a set ofpositive findings for the occurrence of a pulmonary embolism withinimages of a patient's lungs may correspond to output characteristicsthat indicate of the likelihood of an embolism being present withinmultiple images, the areas of the images in which the embolism has beendetected, measurements of the images relating to the condition such asthe size of the embolism within specific images, correlation to otherreported or detected medical conditions that may indicate a presence ofthe embolism, and the like.

Finally, the workflow concludes by the performance of the studyevaluation (operation 570) by a human evaluator using a computerizedevaluation system (e.g., image viewing workstation). The performance ofthis evaluation, and other activities to assign or process thisevaluation, is assisted by the positive or negative findings of themachine learning-identified medical condition. The report or dataresults from the model then may be communicated to the original orderingmedical facility, provided to an evaluator computing system for furtherreview and confirmation, or stored in a results medical informationdatabase. Additional information on the evaluation actions for theimaging study, within the context of evaluator activities in anevaluator user interface, are further described below with reference toFIG. 8.

FIG. 6 illustrates a flowchart 600 of a process for training analgorithm used in detecting a medical condition from image data, such asmay be used in training for a deep learning or other machine learningimage analysis workflow, according to an example described herein. Forexample, it will be understood that the training depicted in theflowchart 600 may be used in connection with the training operation(e.g., operation 510) described within the process of FIG. 5.

As shown in the flowchart 600, the process for training may include:identifying a particular medical condition for training (operation 610),which may involve an automated or human-guided selection of a particularmedical condition based on frequency/occurrence, severity or urgency,identification ability, or like factors of relevance and usability. Theoccurrence of this particular medical condition may be identified inprior reports or diagnostic information, such as with use of naturallanguage processing, text keyword matching, or semantic meaning(operation 620), to determine studies (and training images) in which themedical condition occurs (or does not occur). These studies are thenfurther processed to identify positive or negative findings of theparticular medical condition (operation 630) in the associated trainingimages.

In connection with the identification, further operations may beperformed to anonymize or de-identify protected health information (orother identifying data fields) for use in the training model (operation640). For example, this may include the anonymization or removal ofpatient names, medical record numbers, or other identifyingcharacteristics from the training images and associated reports or orderdata. In some examples, protected health information and otheridentifying information that is included directly within (“burned-in”)pixel data may be detected with the use of optical character recognition(OCR) or other computer-assisted techniques.

Based on the identification of training data from relevant studies andimages (and medical condition findings for such images), a deep learningmodel, or other trainable machine learning algorithm, may be trained(operation 650). This training may include various activities to trainmultiple layers of the deep learning model, through correlations,statistical modeling, and supervisions as applicable. Additionaloperations may occur with use of the trained deep learning model toverify training results and assessment accuracy (operation 660), withsuch verifications potentially occurring during the training process oras adjustments subsequent to detection operations.

It will be understood that variations to the above-described trainingmodel may occur based on the pathologies, conditions, and cases todetect within images that can be considered as critical, hard-to-detect,or abnormal. Further, the training of the model may involve variousoperations to search and analyze report data (and find mostrepresentative images) based on criticality, urgency, image clarity,portrayal, or like characteristics.

FIG. 7 illustrates a flowchart 700 of an example workflow for processingdata of a medical study, based on results from a machine learninganalysis according to an example described herein. The particularsequence depicted in the flowchart 700 is illustrated to emphasizeassignment actions which may occur in image processing and evaluationactivities, such as for a workflow responsible for processing ofradiology images and orders. However, it will be understood that thesequence of operations may vary depending on the precise data operationsto be performed upon the study data produced by the imaging procedureand the originating medical facility, the conditions present whenevaluating the study data, the state of the study data (including thenumber of errors in the study data), and human actions used to effectthe workflow.

The operations depicted in the flowchart 700 include the processing ofdata associated with a medical imaging study (e.g., processing of imagedata and associated metadata or order data originating from a medicalimaging procedure) (operation 710). This data may be processed, forexample, to determine the particular characteristics and type of theimaging procedure, and requirements or conditions for machine evaluationof the procedure. Based on identified information from the study data,the workflow may determine an initial assignment of the medical imagingstudy to one or more evaluator worklists (operation 720). Caching andpre-loading of imaging data to respective evaluators may follow suchassignments or assignment revisions.

The workflow further includes processing the imaging data for themedical imaging study using a deep learning model (operation 730), orother trained machine image recognition algorithm. For example,processing the imaging data may include performing image recognition forone or more medical conditions, identifiable objects, ormachine-detectable scenarios. The workflow may further includeprocessing non-image data associated with the medical imaging studyusing the deep learning model (operation 740) or an evaluative algorithmrelated to the image recognition model. For example, non-image datawhich indicates certain parameters of the medical imaging procedure maybe provided as an input to lower levels of the deep learning algorithm.This non-image data may be processed with use of natural languageprocessing, keyword detection, or like analysis, upon metadata, clinicalhistory and reports, request or order data, and other machine-readableinformation.

As a result of the deep learning algorithm, various indications of amedical condition may be produced. This may include the identificationof a prioritized (e.g., time-sensitive) medical condition (operation750). In some examples, the identification of the medical condition maybe tied or correlated to clinical history or forms of non-imaging data.As a result of the identification of the medical condition, an updatedassignment in the workflow may be implemented (operation 760), forexample, to additional or other medical evaluators, such as based on themedical specialization of evaluators who would be best qualified for amedical evaluation of the identified condition. (It will be understood,however, that the assignment occurring in operations 720 and 760 may bereordered to occur after conducting processing of the image data in thedeep learning model.) In some examples, based on a trauma or severityindication (e.g., provided in the order data or detected by the deeplearning model), images or image series may be unbundled or dividedamong multiple worklists and evaluators (operation 765). A furtherexample of an application of a trauma protocol with use of a traumaindication and the unbundling of images into multiple body regions isdepicted in FIGS. 3 and 4.

As a result of the identification of the prioritized medical condition,various actions may occur to assist the evaluator worklist withdata-driven activities. These may include the prioritization of theassignment within one or more evaluator worklists by the evaluating user(operation 770), and the prioritization of results and reporting for themedical imaging study from the evaluating user back to the requestingmedical facility (operation 780).

Other workflow actions consistent with the use cases described hereinmay also be implemented. For example, with use of natural languageprocessing (NLP) of clinical history and order information (e.g., in theevaluation of non-image data in operation 740), false positives andpreviously known findings may be identified but excluded from studyprioritization. Likewise, these and other types of non-image data mightbe used in training the model, or in excluding or redirecting theresults of the model at lower levels of a deep learning model.

FIG. 8 provides an illustration of a flowchart 800 of additionalworkflow operations that may be performed upon a particular imagingstudy (and associated imaging data) at an evaluator computingworkstation, or for performance assisted by an evaluator, based on themedical condition identification scenarios described herein. Forexample, these workflow operations may be implemented in portions of aviewer, reporting, or other evaluative graphical user interface andassociated computer system processes.

As illustrated, the various operations of flowchart 800 may includeprocessing operations that are performed to accomplish evaluation thatis assisted by the results of the machine learning algorithm(specifically, a deep learning model analysis). For example, theoperations may include: receiving results of the deep learning modelanalysis for the particular medical imaging study (operation 810), whichmay include an indication (or a non-indication) of a specific medicalcondition, a relevance/likelihood/confidence score of the specificmedical condition, identified findings of the specific medicalcondition, and the like. Based on the indication and associated workflowmodifications, the study may be prioritized within the worklist of theevaluator (operation 820), such as to receive a higher location,highlight, or alert in a worklist graphical user interface.

Based on the prioritization and the indication of the identified medicalcondition, various operations may occur within the graphical userinterface of the evaluating user to modify the display of the study(operation 830). The changes to the display of the study may includeproviding computer-generated modifications, groupings, annotations, ormodified arrangement of the display of the images. Additionally,operations to assist the evaluation of the study based on the specificcondition may be implemented in an evaluation graphical user interface,such as the pre-selection of report content (operation 840).

The workflow of flowchart 800 concludes with the performance of thestudy evaluation through human and machine operations (operation 850)through a combination of evaluator and automated processing actions, andthe tracking of verification of findings from the performance of thestudy evaluation (operation 860) to store and capture data produced as aresult of the evaluator. For example, an evaluator may verify that themedical condition was correctly (or incorrectly) detected by the machinelearning model, or provide feedback and condition state modification forreport generation purposes and further model training. In furtherexamples, an evaluator may designate a particular condition fortraining, educational, or further review purposes (such as to designatea particular detected condition within an image as an unambiguous andclear state of a certain medical condition).

The presently described prioritization and indications of the identifiedmedical condition may assist with other aspects of the evaluation andreporting activities. For example, a computer-aided location orannotation of an anatomical feature (or a medical condition finding) maybe designated by the machine learning model for display within anevaluation image viewer interface. As another example, acomputer-determined preferred phrasing or textual output may bedesignated by the machine learning model for use in a reportinginterface. As another example, key images which indicate the medicalcondition may be designated by the machine learning model for use in anevaluation image viewer interface or in an electronic version of areport. Other variations may occur that identify images of importanceand modify a presentation state based on results of the machine learningmodel.

Other variations or additional actions for the previously describedworkflow may be performed using the results of the machine learningmodel. For example, triggering a phone notification workflow based uponthe image analysis results. With an internal head bleed that is detectedby the machine learning model, the phone call could be initiated beforea radiologist has even opened the study on his or her workstation inorder to reduce the amount time it takes to contact the referringphysician. When the call connects, the radiologist could access andreview the images in real-time (if he or she hasn't read them yet) andprovide a confirmation of the head bleed from the respective images.Similar alerts, graphical user interface actions, and electronicfunctions may be performed in a workflow based on such results of themachine learning model.

FIG. 9 illustrates an example configuration of a system architecture 900configured to implement the presently described processing systemaccording to an example described herein. System architecture 900 mayimplement components such as the imaging order processing system 102 andfeatures of the image review system 106 and the machine learninganalysis system 108. The system architecture 900 may include features ofa radiology information system 920, a picture archiving communicationsystem 930, order data processing 940, image data processing 950, imagedata detection 960, machine learning training 970, machine learningverification 980, study data management 992, study assignment 994, studyviewing 996, and study reporting 998. In operation with these features,the system architecture 900 may further include a plurality of databasesor data stores, including a medical imaging database 902, a machinelearning model database 904, a study analysis database 906, and a studyworkflow database 908. Such features may be embodied by any number ofsoftware or hardware forms (including through the use of physical orlogical blocks of computer instructions, which may, for instance, beorganized as an object, procedure, or function).

The medical imaging database 902 may provide a location for storage ofimaging data (and metadata) for medical imaging procedures andassociated studies. The machine learning model database 904 may providea location for storage of deep learning models, inputs, and relevantparameters for operation of the machine learning algorithms. The studyanalysis database 906 may provide a location for storage of informationfor study evaluation states of particular studies, preferences for studyevaluations, and other data fields used to assist the study evaluationin response to the performance of the machine learning models. The studyworkflow database 908 may provide a location for storage of informationfor workflow states of particular studies, preferences for workflowoperations, and other data fields used to assist the workflow operationsoccurring in response to the performance of the machine learning models.

The respective features of the system architecture 900 may performfunctional operations to effect the processing, image identification,and workflow management techniques described herein. For example, theradiology information system 920 may be used to provide respectiveinformation processing functions of a RIS. The picture archivingcommunication system 930 may be used to provide image storage and accessfeatures of a Picture Archiving Communication System (PACS). The orderdata processing 940 may be used to process orders, and determinerelevant information for non-image data of studies. The image dataprocessing 950 may be used to request, receive, validate, and storeimages data of studies.

The image data processing 950 may be used to perform imaging processingoperations on imaging data obtained from a set of data associated with amedical imaging procedure, or from a customer imaging device, an imagearchive, medical facility data store, or other imaging data source, suchas with processing operations that provide image data as input to themachine learning models. The image data detection 960 may be used toimplement the machine learning model, such as with performance of a deeplearning model to detect certain medical conditions within the images ofthe study image data.

The machine learning training 970 may be used to implement training ofthe machine learning model, such as with the use of imaging andnon-imaging data completed by previous study evaluations. The machinelearning verification 980 may be used to compare results of the imagedetection operations with results of the study evaluations, and tomodify the machine learning model based on verification of outcomes. Thestudy data management 992 may be used to coordinate the transmission andtransfer of image and non-image data associated with an imaging studybased on the results of the image detection operations and the adjustedworkflows. The study assignment 994 may be used to provide assignmentsto one or more evaluators (and to facilitate the transfer of data tocomputing systems associated with the one or more evaluators) based onthe results of the image detection operations and the adjustedworkflows. The study viewing 996 may be used to view studies (andspecific types of rendering data) on screen by an evaluating user, whichmay be influenced by the detection of certain medical conditions,prioritization, or other results of the image detection operations andthe adjusted workflows. The study reporting 998 may be used to establishreporting functions for the evaluating user, from report informationthat is created or contributed to by the evaluating user, with suchreport information being influenced or assisted by the results of theimage detection operations and the adjusted workflows.

FIG. 10 is a block diagram illustrating an example computing system 1000upon which any one or more of the methodologies herein discussed may berun according to an example described herein. Computer system 1000 maybe embodied as a computing device, providing operations of thecomponents featured in the various figures, including components of theimaging order processing system 102, the imaging system 104, the imagereview system 106, the machine learning analysis system 108, componentsand data storage elements in system architecture 900, or any otherprocessing or computing platform or component described or referred toherein. In alternative embodiments, the machine operates as a standalonedevice or may be connected (e.g., networked) to other machines. In anetworked deployment, the machine may operate in the capacity of eithera server or a client machine in server-client network environments, orit may act as a peer machine in peer-to-peer (or distributed) networkenvironments. The computer system machine may be a personal computer(PC) that may or may not be portable (e.g., a notebook or a netbook), atablet, a Personal Digital Assistant (PDA), a mobile telephone orsmartphone, a web appliance, or any machine capable of executinginstructions (sequential or otherwise) that specify actions to be takenby that machine. Further, while only a single machine is illustrated,the term “machine” shall also be taken to include any collection ofmachines that individually or jointly execute a set (or multiple sets)of instructions to perform any one or more of the methodologiesdiscussed herein.

Example computer system 1000 includes a processor 1002 (e.g., a centralprocessing unit (CPU), a graphics processing unit (GPU) or both), a mainmemory 1004 and a static memory 1006, which communicate with each othervia an interconnect 1008 (e.g., a link, a bus, etc.). The computersystem 1000 may further include a video display unit 1010, analphanumeric input device 1012 (e.g., a keyboard), and a user interface(UI) navigation device 1014 (e.g., a mouse). In one embodiment, thevideo display unit 1010, input device 1012 and UI navigation device 1014are a touch screen display. The computer system 1000 may additionallyinclude a storage device 1016 (e.g., a drive unit), a signal generationdevice 1018 (e.g., a speaker), an output controller 1032, and a networkinterface device 1020 (which may include or operably communicate withone or more antennas 1030, transceivers, or other wirelesscommunications hardware), and one or more sensors 1028.

The storage device 1016 includes a machine-readable medium 1022 on whichis stored one or more sets of data structures and instructions 1024(e.g., software) embodying or utilized by any one or more of themethodologies or functions described herein. The instructions 1024 mayalso reside, completely or at least partially, within the main memory1004, static memory 1006, and/or within the processor 1002 duringexecution thereof by the computer system 1000, with the main memory1004, static memory 1006, and the processor 1002 constitutingmachine-readable media.

While the machine-readable medium 1022 is illustrated in an exampleembodiment to be a single medium, the term “machine-readable medium” mayinclude a single medium or multiple media (e.g., a centralized ordistributed database, and/or associated caches and servers) that storethe one or more instructions 1024. The term “machine-readable medium”shall also be taken to include any tangible medium that is capable ofstoring, encoding or carrying instructions for execution by the machineand that cause the machine to perform any one or more of themethodologies of the present disclosure or that is capable of storing,encoding or carrying data structures utilized by or associated with suchinstructions. The term “machine-readable medium” shall accordingly betaken to include, but not be limited to, solid-state memories, opticalmedia, and magnetic media. Specific examples of machine-readable mediainclude non-volatile memory, including, by way of example, semiconductormemory devices (e.g., Electrically Programmable Read-Only Memory(EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM))and flash memory devices; magnetic disks such as internal hard disks andremovable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks.

The instructions 1024 may further be transmitted or received over acommunications network 1026 using a transmission medium via the networkinterface device 1020 utilizing any one of a number of well-knowntransfer protocols (e.g., HTTP). Examples of communication networksinclude a local area network (LAN), wide area network (WAN), theInternet, mobile telephone networks, Plain Old Telephone (POTS)networks, and wireless data networks (e.g., Wi-Fi, 3G, and 4G LTE/LTE-Aor WiMAX networks). The term “transmission medium” shall be taken toinclude any intangible medium that is capable of storing, encoding, orcarrying instructions for execution by the machine, and includes digitalor analog communications signals or other intangible medium tofacilitate communication of such software.

Other applicable network configurations may be included within the scopeof the presently described communication networks. Although exampleswere provided with reference to a local area wireless networkconfiguration and a wide area Internet network connection, it will beunderstood that communications may also be facilitated using any numberof personal area networks, LANs, and WANs, using any combination ofwired or wireless transmission mediums.

The embodiments described above may be implemented in one or acombination of hardware, firmware, and software. For example, thefeatures in the system architecture 900 of the processing system may beclient-operated software or be embodied on a server running an operatingsystem with software running thereon. While some embodiments describedherein illustrate only a single machine or device, the terms “system”,“machine”, or “device” shall also be taken to include any collection ofmachines or devices that individually or jointly execute a set (ormultiple sets) of instructions to perform any one or more of themethodologies discussed herein.

Examples, as described herein, may include, or may operate on, logic ora number of components, modules, features, or mechanisms. Such items aretangible entities (e.g., hardware) capable of performing specifiedoperations and may be configured or arranged in a certain manner. In anexample, circuits may be arranged (e.g., internally or with respect toexternal entities such as other circuits) in a specified manner as amodule, component, or feature. In an example, the whole or part of oneor more computer systems (e.g., a standalone, client or server computersystem) or one or more hardware processors may be configured by firmwareor software (e.g., instructions, an application portion, or anapplication) as an item that operates to perform specified operations.In an example, the software may reside on a machine readable medium. Inan example, the software, when executed by underlying hardware, causesthe hardware to perform the specified operations.

Accordingly, such modules, components, and features are understood toencompass a tangible entity, be that an entity that is physicallyconstructed, specifically configured (e.g., hardwired), or temporarily(e.g., transitorily) configured (e.g., programmed) to operate in aspecified manner or to perform part or all of any operation describedherein. Considering examples in which modules, components, and featuresare temporarily configured, each of the items need not be instantiatedat any one moment in time. For example, where the modules, components,and features comprise a general-purpose hardware processor configuredusing software, the general-purpose hardware processor may be configuredas respective different items at different times. Software mayaccordingly configure a hardware processor, for example, to constitute aparticular item at one instance of time and to constitute a differentitem at a different instance of time.

Additional examples of the presently described method, system, anddevice embodiments are suggested according to the structures andtechniques described herein. Other non-limiting examples may beconfigured to operate separately, or can be combined in any permutationor combination with any one or more of the other examples provided aboveor throughout the present disclosure.

1. (canceled)
 2. A method of artificial intelligence data processing formedical imaging data, comprising operations performed by a computingdevice, with the operations comprising: obtaining imaging dataoriginating from a medical imaging procedure of a human subject;classifying, using a trained image recognition model, at least oneidentifiable condition from at least one image of the imaging data,wherein the selection and operation of the trained image recognitionmodel is based on at least one characteristic of the medical imagingprocedure; and defining properties of an electronic workflow thatperforms a diagnostic evaluation of the imaging data from the medicalimaging procedure, based on at least one classified characteristic ofthe identifiable condition.
 3. The method of claim 2, wherein theselection of the trained image recognition model is provided byselecting the trained image recognition model from a plurality oftrained image recognition models, based on metadata originating from themedical imaging procedure.
 4. The method of claim 3, wherein themetadata indicates an anatomical area or anatomical feature that isrepresented in the at least one image, and wherein the operation of thetrained image recognition model on the at least one image is modifiedbased on the anatomical area or anatomical feature that is representedin the at least one image.
 5. The method of claim 2, wherein theclassified characteristic of the identifiable condition includes atleast one of: an identification of an area in an image in which theidentifiable condition is detected, a likelihood of a medical conditionbeing present in an image, a measurement in an image associated with amedical condition, a correlation of a first medical condition to asecond medical condition detected in the imaging data, or a measurementof a: frequency, severity, or urgency of a medical condition detected inan image.
 6. The method of claim 2, wherein the properties of theelectronic workflow are further defined based on results of naturallanguage processing, the results of natural language processing beingproduced from analysis of data associated with the medical imagingprocedure or data associated with a prior medical imaging procedure ofthe human subject.
 7. The method of claim 2, wherein the classifiedcharacteristic of the identifiable condition includes a detection valuethat corresponds to a level of feature recognition in the image data fora negative or positive finding of the identifiable condition, wherein atleast one property in the electronic workflow is modified based on thedetection value.
 8. The method of claim 2, wherein defining theelectronic workflow includes: communicating data to establish, with theelectronic workflow, a first assignment of the imaging data to a firstevaluator; and communicating data to establish, with the electronicworkflow, a second assignment of the imaging data to a second evaluator;wherein respective properties of the first assignment and the secondassignment are defined based on the classified characteristic of theidentifiable condition.
 9. The method of claim 2, wherein defining theelectronic workflow includes: determining an assignment, for at least afirst part of the imaging data, within a first electronic worklistassociated with a first evaluator, wherein the classified characteristicof the identifiable condition includes an indication of an urgentmedical condition; and communicating an indication of the urgent medicalcondition in connection with the assignment for the first part of theimaging data.
 10. The method of claim 9, wherein the urgent medicalcondition is indicated as a trauma or other time-sensitive medicalcondition, and wherein defining the electronic workflow includesdetermining a second assignment, for at least a second part of theimaging data, within a second electronic worklist associated with asecond evaluator, wherein the first part of the imaging data and thesecond part of the imaging data are identified based on respectiveanatomical regions captured by the imaging data.
 11. The method of claim2, further comprising: identifying, from the imaging data, the at leastone image as being associated with an anatomical classification or area,wherein the operation of the trained image recognition model is furtherbased on the anatomical classification or area.
 12. A non-transitorymachine-readable storage medium including instructions that, whenexecuted by at least one processor of a computing device, causes thecomputing device to perform operations comprising: obtaining imagingdata originating from a medical imaging procedure of a human subject;classifying, using a trained image recognition model, at least oneidentifiable condition from at least one image of the imaging data,wherein the selection and operation of the trained image recognitionmodel is based on at least one characteristic of the medical imagingprocedure; and defining properties of an electronic workflow thatperforms a diagnostic evaluation of the imaging data from the medicalimaging procedure, based on at least one classified characteristic ofthe identifiable condition.
 13. The machine-readable storage medium ofclaim 12, wherein the selection of the trained image recognition modelis provided by selecting the trained image recognition model from aplurality of trained image recognition models, based on metadataoriginating from the medical imaging procedure, and wherein the metadataindicates an anatomical area or anatomical feature that is representedin the at least one image.
 14. The machine-readable storage medium ofclaim 12, wherein the classified characteristic of the identifiablecondition includes at least one of: an identification of an area in animage in which the identifiable condition is detected, a likelihood of amedical condition being present in an image, a measurement in an imageassociated with a medical condition, a correlation of a first medicalcondition to a second medical condition detected in the imaging data, ora measurement of a: frequency, severity, or urgency of a medicalcondition detected in an image.
 15. The machine-readable storage mediumof claim 12, wherein the properties of the electronic workflow arefurther defined based on results of natural language processing, theresults of natural language processing being produced from analysis ofdata associated with the medical imaging procedure or data associatedwith a prior medical imaging procedure of the human subject.
 16. Themachine-readable storage medium of claim 12, wherein defining theelectronic workflow includes: communicating data to establish, with theelectronic workflow, a first assignment of the imaging data to a firstevaluator; and communicating data to establish, with the electronicworkflow, a second assignment of the imaging data to a second evaluator;wherein respective properties of the first assignment and the secondassignment are defined based on the classified characteristic of theidentifiable condition; and wherein the classified characteristicincludes a detection value that corresponds to a negative or positivefinding of the identifiable condition, and wherein at least one propertyin the electronic workflow is modified based on the detection value. 17.The machine-readable storage medium of claim 12, wherein defining theelectronic workflow includes: determining an assignment, for at least afirst part of the imaging data, within a first electronic worklistassociated with a first evaluator, wherein the classified characteristicof the medical condition includes an indication of an urgent medicalcondition; and communicating an indication of the urgent medicalcondition in connection with the assignment for the first part of theimaging data; determining a second assignment, for at least a secondpart of the imaging data, within a second electronic worklist associatedwith a second evaluator; wherein the first part of the imaging data andthe second part of the imaging data are identified based on respectiveanatomical regions captured by the imaging data.
 18. Themachine-readable storage medium of claim 12, the operations furthercomprising: identifying, from the imaging data, the at least one imageas being associated with an anatomical classification or area, whereinthe operation of the trained image recognition model is further based onthe anatomical classification or area.
 19. A computing system,comprising: processing circuitry; and memory comprising instructionsstored thereon, which when executed by the processing circuitry,configure the computing system to perform operations comprising:obtaining imaging data originating from a medical imaging procedure of ahuman subject; classifying, using a trained image recognition model, atleast one identifiable condition from at least one image of the imagingdata, wherein the selection and operation of the trained imagerecognition model is based on at least one characteristic of the medicalimaging procedure; and defining properties of an electronic workflowthat performs a diagnostic evaluation of the imaging data from themedical imaging procedure, based on at least one classifiedcharacteristic of the identifiable condition.
 20. The computing systemof claim 19, wherein the selection of the trained image recognitionmodel is provided by selecting the trained image recognition model froma plurality of trained image recognition models, based on metadataoriginating from the medical imaging procedure, and wherein the metadataindicates an anatomical area or anatomical feature that is representedin the at least one image.
 21. The computing system of claim 19, whereinthe classified characteristic of the identifiable condition includes atleast one of: an identification of an area in an image in which theidentifiable condition is detected, a likelihood of a medical conditionbeing present in an image, a measurement in an image associated with amedical condition, a correlation of a first medical condition to asecond medical condition detected in the imaging data, or a measurementof a: frequency, severity, or urgency of a medical condition detected inan image.
 22. The computing system of claim 19, wherein the propertiesof the electronic workflow are further defined based on results ofnatural language processing, the results of natural language processingbeing produced from analysis of data associated with the medical imagingprocedure or data associated with a prior medical imaging procedure ofthe human subject.
 23. The computing system of claim 19, whereindefining the electronic workflow includes: communicating data toestablish, with the electronic workflow, a first assignment of theimaging data to a first evaluator; and communicating data to establish,with the electronic workflow, a second assignment of the imaging data toa second evaluator; wherein respective properties of the firstassignment and the second assignment are defined based on the classifiedcharacteristic of the identifiable condition; and wherein the classifiedcharacteristic includes a detection value that corresponds to a negativeor positive finding of the identifiable condition, and wherein at leastone property in the electronic workflow is modified based on thedetection value.
 24. The computing system of claim 19, wherein definingthe electronic workflow includes: determining an assignment, for atleast a first part of the imaging data, within a first electronicworklist associated with a first evaluator, wherein the classifiedcharacteristic of the identifiable condition includes an indication ofan urgent medical condition; and communicating an indication of theurgent medical condition in connection with the assignment for the firstpart of the imaging data; determining a second assignment, for at leasta second part of the imaging data, within a second electronic worklistassociated with a second evaluator; wherein the first part of theimaging data and the second part of the imaging data are identifiedbased on respective anatomical regions captured by the imaging data. 25.The computing system of claim 19, the operations further comprising:identifying, from the imaging data, the at least one image as beingassociated with an anatomical classification or area, wherein theoperation of the trained image recognition model is further based on theanatomical classification or area.